Senior Backend Engineer / Senior Software Engineer
📍 Doha, Qatar · 🌍 Remote-friendly

I’m a backend and data engineer with 7+ years of experience building scalable, fault-tolerant systems and real-time data pipelines. My work sits at the intersection of backend architecture, data engineering, and production ML, with a strong focus on performance, reliability, and systems that keep working as data grows.

I enjoy solving problems where scale, data volume, and system design actually matter.

• Backend architecture for data-intensive platforms
• High-throughput ingestion & ETL pipelines
• Clean, scalable API design (performance-aware)
• Data modeling for large, highly-connected datasets
• Production integration of ML / lightweight LLMs
• Observability, reliability, and operational simplicity

Working on the backend platform powering end-to-end SBOM lifecycle management for enterprise customers. I design ingestion pipelines for CycloneDX and SPDX, model complex component graphs, and build APIs for trust centers, public/private artifact publishing, and compliance-ready distribution. A big part of my work is improving performance and reliability through pagination-first APIs, query optimization, caching, and sane payload limits. I also actively contribute to sbomify’s open-source core.

Worked on Brave News, building and scaling backend new systems serving 200K+ MAUs across multiple geographies. Designed FastAPI-based microservices on AWS EKS, built Kafka-driven aggregation pipelines, and integrated lightweight NLP/ML models for topic clustering, categorization, and recommendations. Focused heavily on performance, observability, and globally scalable data pipelines.

Built and scaled backend systems for large SaaS platforms, including Lola.com (corporate travel & expense management). Worked on high-volume data pipelines, ETL systems, API services, and automation using Python, Django, FastAPI, and cloud-native tooling, with a strong emphasis on data quality, reliability, and system performance.

• IEEE CloudCom 2022 — Scalable Containerized Pipeline for Real-time Big Data Analytics
  Achieved 2.4× throughput and 80× latency reduction using Kubernetes-based pipelines.

Research interests include small/efficient LLMs, and resource-aware ML pipelines.

• Mentor junior engineers and run internal tech talks
• Chess.com rating: 1700+
• Enjoy building systems that stay fast after the dataset grows

• Email: [email protected]
• LinkedIn: https://www.linkedin.com/in/rana-aurangzaib-45557b135
• GitHub: https://github.com/aurangzaib048